• Shamanth R Shrivathsa
• Shailesh M R
• Dr. Rajeswara Rao K V S

People, living in cities like Bengaluru have to plan their daily travel schedule against the congested traffic. Studies report that the travel time in Bengaluru increases by 6% for every quarter of the year. Travel time prediction enhances the passengers to plan their daily routine. Several tools such as machine learning, ARIMA model, MARE, MAPPE, cluster algorithms, ANN are used by the researchers for the travel time prediction. In this work, the authors propose Artificial Neural Algorithm for the travel time prediction, which exhibits a very high accuracy in the results. The real time data of Metropolitan Transport Service has been collected for a route from Kempegowda Bus Station to Kempegowda International Airport in Bengaluru. The length of this route is 34.5km. The method which the authors propose for the travel time prediction will be more accurate as more data sets appear in the algorithm. Passengers can easily plan their schedule according to the predicted travel time and also transportation authorities can plan their operations and schedules for providing better service.

Travel time prediction, Artificial Neural Network, Bengaluru Metropolitan Transport Corporation, Public transport

"TRAVEL TIME PREDICTION OF PUBLIC TRANSPORT IN A SELECTED ROUTE USING ARTIFICIAL NEURAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 2, page no.277-283, February-2019, Available :http://www.jetir.org/papers/JETIR1902742.pdf

"TRAVEL TIME PREDICTION OF PUBLIC TRANSPORT IN A SELECTED ROUTE USING ARTIFICIAL NEURAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 2, page no. pp277-283, February-2019, Available at : http://www.jetir.org/papers/JETIR1902742.pdf